Causal Intervention in Concept Bottleneck Models

Concept Bottleneck Models (CBMs) are a class of interpretable AI models that enable concept-level control over the decisionmaking process, allowing humans to directly intervene in model predictions. As a result, CBMs have been widely adopted in tasks requiring interpretability and controllability. Existing research has demonstrated that human-model interaction can signi(cid:28)cantly enhance CBM performance. However, in practical applications, each intervention requires manual human e(cid:27)ort and explicit data provision. Therefore, a central challenge is how to improve model performance while minimizing intervention e(cid:27)ort. In this paper, we show that trained CBM parameters encode usable concept-correlation structure. We leverage this structure during interaction by propagating user feedback through the shared inputto-concept pathway, thereby adjusting non-intervened concepts without training an additional realignment model. Experimental results demonstrate that our method improves low-intervention performance across multiple models and datasets. Compared with all tested baselines, our approach improves early-intervention performance by approximately 9%; compared with Concept Realignment, it improves AUC@10 and AUC@20 by 3.90% and 2.88%, respectively, while avoiding extra training. Code is available at https://github.com/LMBTough/CI. • •